# Customize your maps

#### Richard Aubrey White, Chi Zhang

#### 2021-01-15

    library(csmaps)
    library(ggplot2)
    library(data.table)
    library(magrittr)

## Colored maps

### Automatic coloring by `location_code`

    pd <- copy(csmaps::nor_county_map_b2024_split_dt)

    q <- ggplot()
    q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
    q <- q + geom_polygon(
      data = pd,
      mapping = aes(
        x = long,
        y = lat,
        group = group,
        fill = location_code
      ),
      color="black",
      linewidth = 0.4
    )
    q <- q + theme_void()
    q <- q + coord_quickmap()
    q <- q + labs(title = "")
    q

### Customized coloring with external information

It is also possible to specify the color by user-defined groups. Here we show an example of assigning different (pseudo) risk level to each county.

    pd <- copy(csmaps::nor_county_map_b2024_split_dt)

    # assign each location a random category for different colors
    location_info <- unique(pd[,c("location_code")])
    location_info[,category:=rep(
      c("Good","Normal","Neutral","Bad","Very Bad"),
      each=3)[1:.N]
    ]
    location_info[,category:=factor(
      category,
      levels=c("Good","Normal","Neutral","Bad","Very Bad")
      )
    ]
    print(location_info)
    #>     location_code category
    #>            <char>   <fctr>
    #>  1:  county_nor03     Good
    #>  2:  county_nor11     Good
    #>  3:  county_nor15     Good
    #>  4:  county_nor18   Normal
    #>  5:  county_nor31   Normal
    #>  6:  county_nor32   Normal
    #>  7:  county_nor33  Neutral
    #>  8:  county_nor34  Neutral
    #>  9:  county_nor39  Neutral
    #> 10:  county_nor40      Bad
    #> 11:  county_nor42      Bad
    #> 12:  county_nor46      Bad
    #> 13:  county_nor50 Very Bad
    #> 14:  county_nor55 Very Bad
    #> 15:  county_nor56 Very Bad

    # join the map data.table
    pd[
      location_info,
      on="location_code",
      category:=category
    ]

    q <- ggplot()
    q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
    q <- q + geom_polygon(
      data = pd,
      mapping = aes(
        x = long,
        y = lat,
        group = group,
        fill = category
      ),
      color="black",
      linewidth = 0.4
    )
    q <- q + coord_quickmap()
    q <- q + labs(title="")
    q <- q + theme_void()
    q

## Labeled maps

We can add labels of county index onto the maps. There are several options for adding texts on a graph in `ggplot2`. We recommend `geom_label()` to add the labels if no label overlap occurs, otherwise we recommend using `ggrepel::geom_label_repel()`.

    q <- ggplot()
    q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
    q <- q + geom_polygon(
      data = pd,
      mapping = aes(
        x = long,
        y = lat,
        group = group,
        fill = category
      ),
      color="black",
      linewidth = 0.4
    )
    q <- q + geom_label(
      data = csmaps::nor_county_position_geolabels_b2024_split_dt,
      mapping = aes(
        x = long,
        y = lat,
        label = location_code
      )
    )
    # ggrepel::geom_label_repel() for avoiding overlap
    q <- q + theme_void()
    q <- q + coord_quickmap()
    q <- q + labs(title = "")
    q

Labels can be easily added to other layouts, such as Oslo wards.

    q <- ggplot(mapping = aes(x = long, y = lat))
    q <- q + geom_polygon(
      data = csmaps::oslo_ward_map_b2024_default_dt,
      mapping = aes(group = group),
      color = "black",
      fill = "white",
      linewidth = 0.4
    )
    q <- q + geom_label(
      data = csmaps::oslo_ward_position_geolabels_b2024_default_dt,
      mapping = aes(label = location_code),
      color = "red",
      size = 3,
      label.size = 0.1,
      label.r = grid::unit(0, "lines")
    )
    q <- q + theme_void()
    q <- q + coord_quickmap()
    q

## Enrich plot with additional data

It is convenient to use `csdata` package to enrich Norway and Oslo maps with external information, such as location name and population.

### Add county name and population to Norway map

    # enrich with population and location name
    dpop_2024 <- csdata::nor_population_by_age_cats()[
      calyear==2024 &
      granularity_geo %in% "county"
    ]

    # join, create label
    labels <- copy(csmaps::nor_county_position_geolabels_b2024_split_dt)
    labels[
      dpop_2024,
      on = "location_code",
      pop_total := pop_jan1_n
    ]
    labels[
      csdata::nor_locations_names(),
      on = "location_code",
      location_name_short := location_name_short
    ]
    labels[, label := paste0(location_name_short, '\n', pop_total)]
    print(head(labels))
    #>    location_code  long      lat  repel pop_total location_name_short
    #>           <char> <num>    <num> <lgcl>     <num>              <char>
    #> 1:  county_nor31 11.50 59.00000   TRUE    312152                 ØFO
    #> 2:  county_nor32 11.20 60.03851   TRUE    728803                 AKR
    #> 3:  county_nor33  8.85 60.60000   TRUE    269819                 BUS
    #> 4:  county_nor03  2.20 60.30000  FALSE    717710                 OSL
    #> 5:  county_nor34 11.10 61.90000  FALSE    376304                 INN
    #> 6:  county_nor39 10.00 59.32481   TRUE    256432                 VFO
    #>          label
    #>         <char>
    #> 1: ØFO\n312152
    #> 2: AKR\n728803
    #> 3: BUS\n269819
    #> 4: OSL\n717710
    #> 5: INN\n376304
    #> 6: VFO\n256432

    # plot
    pd <- copy(csmaps::nor_county_map_b2024_split_dt)

    q <- ggplot()
    q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
    q <- q + geom_polygon(
      data = pd,
      mapping = aes(
        x = long,
        y = lat,
        group = group,
        fill = location_code
      ),
      color="black",
      linewidth = 0.4
    )
    q <- q + ggrepel::geom_label_repel(
      data = labels[repel==TRUE],
      mapping = aes(x = long, y = lat, label = label),
      size = 3,
      label.size = 0.1,
      label.r = grid::unit(0, "lines"),
      min.segment.length = 0
    )
    q <- q + geom_label(
      data = labels[repel==FALSE],
      mapping = aes(x = long, y = lat, label = label),
      size = 3,
      label.size = 0.1,
      label.r = grid::unit(0, "lines")
    )
    q <- q + theme_void()
    q <- q + coord_quickmap()
    q <- q + labs(title = "")
    q

### Add location name for ward and population for Oslo map

    # enrich with population and location name
    dpop_2024 <- csdata::nor_population_by_age_cats()[calyear==2024]

    # join, create label
    labels <- copy(csmaps::oslo_ward_position_geolabels_b2024_default_dt)
    labels[
      dpop_2024,
      on = "location_code",
      pop_total := pop_jan1_n
    ]
    labels[
      csdata::nor_locations_names(),
      on = "location_code",
      location_name_short := location_name_short
    ]
    labels[, label := paste0(location_name_short, '\n', pop_total)]
    print(head(labels))
    #>         location_code     long      lat pop_total location_name_short
    #>                <char>    <num>    <num>     <num>              <char>
    #> 1: wardoslo_nor030101 10.72000 59.89000     63712            Gml Oslo
    #> 2: wardoslo_nor030102 10.78000 59.92567     65577               Grünl
    #> 3: wardoslo_nor030103 10.76683 59.93981     47660                Sagn
    #> 4: wardoslo_nor030104 10.73555 59.91230     40095            St. Hans
    #> 5: wardoslo_nor030105 10.66500 59.89925     60646             Frogner
    #> 6: wardoslo_nor030106 10.65000 59.92500     35425              Ullern
    #>              label
    #>             <char>
    #> 1: Gml Oslo\n63712
    #> 2:    Grünl\n65577
    #> 3:     Sagn\n47660
    #> 4: St. Hans\n40095
    #> 5:  Frogner\n60646
    #> 6:   Ullern\n35425

    q <- ggplot(mapping = aes(x = long, y = lat))
    q <- q + geom_polygon(
      data = csmaps::oslo_ward_map_b2024_default_dt,
      mapping = aes(group = group),
      color = "black",
      fill = "white",
      linewidth = 0.4
    )
    q <- q + geom_label(
      data = labels,
      mapping = aes(label = label),
      color = "red",
      size = 3,
      label.size = 0.1,
      label.r = grid::unit(0, "lines")
    )
    q <- q + theme_void()
    q <- q + coord_quickmap()
    q